Results 161 to 170 of about 2,291,078 (312)

COMET-QE and Active Learning for Low-Resource Machine Translation [PDF]

, 2022
Everlyn Asiko Chimoto, Bruce A. Bassett
openalex   +3 more sources

ALiPy: Active Learning in Python [PDF]

arXiv, 2019
Supervised machine learning methods usually require a large set of labeled examples for model training. However, in many real applications, there are plentiful unlabeled data but limited labeled data; and the acquisition of labels is costly. Active learning (AL) reduces the labeling cost by iteratively selecting the most valuable data to query their ...
arxiv  

Lightweight Block Cipher Security Evaluation Based on Machine Learning Classifiers and Active S-Boxes [PDF]

, 2021
Ting Rong Lee, Je Sen Teh, Norziana Jamil, Jasy Liew Suet Yan, Jiageng Chen   +4 more
openalex   +1 more source

Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics

Advanced Engineering Materials, EarlyView.
Machine learning (ML) is revolutionizing the development of bioinspired ceramics. This article investigates how ML can be used to design new ceramic materials with exceptional performance, inspired by the structures found in nature. The research highlights how ML can predict material properties, optimize designs, and create advanced models to unlock a ...
Hamidreza Yazdani Sarvestani, Aditi Singh, Behnam Ashrafi   +2 more
wiley   +1 more source

Active Learning in Example-Based Machine Translation

, 2009
In data-driven Machine Translation approaches, like Example-Based Machine Translation (EBMT) (Brown, 2000) and Statistical Machine Translation (Vogel et al., 2003), the quality of the translations produced depends on the amount of training data available.
Gangadharaiah, Rashmi, Brown, Ralf D., Carbonell, Jaime   +2 more
openaire   +3 more sources

Consumer Preference Elicitation of Complex Products Using Fuzzy Support Vector Machine Active Learning

Marketing science (Providence, R.I.), 2016
As technology advances, new products (e.g., digital cameras, computer tablets, etc.) have become increasingly more complex. Researchers often face considerable challenges in understanding consumers’ preferences for such products.
Dongling Huang, Lan Luo
semanticscholar   +1 more source

Advances in Hybrid Icing and Frosting Protection Strategies for Optics, Lens, and Photonics in Cold Environments Using Thin‐Film Acoustic Waves

Advanced Engineering Materials, EarlyView.
This article provides a comprehensive overview of fundamentals and recent advances of transparent thin‐film surface acoustic wave technologies on glass substrates for monitoring and prevention/elimination of fog, ice, and frost. Fogging, icing, or frosting on optical lenses, optics/photonics, windshields, vehicle/airplane windows, and solar panel ...
Hui Ling Ong, Zhangbin Ji, Luke Haworth, Yihao Guo, Jaime del Moral, Stefan Jacob, Ana Borras, Agustin R. Gonzalez‐Elipe, Jikai Zhang, Jian Zhou, Glen McHale, Yong‐Qing Fu   +11 more
wiley   +1 more source

MMR-based active machine learning for bio named entity recognition [PDF]

, 2006
Seokhwan Kim, Yu Song, Kyungduk Kim, Jeong-Won Cha, Gary Geunbae Lee   +4 more
openalex   +1 more source

Understanding Heating in Active Region Cores through Machine Learning. II. Classifying Observations [PDF]

, 2021
Will Barnes, S. J. Bradshaw, N. M. Viall
openalex   +1 more source

Active Learning of Extended Finite State Machines [PDF]

, 2012
Once they have high-level models of the behavior of software components, engineers can construct better software in less time. A key problem in practice, however, is the construction of models for existing software components, for which no or only limited documentation is available.
openaire   +3 more sources